Mesoscale modeling refers to weather models for domain areas from 10-1000 kilometers, used for predicting future weather conditions. Generally, there are several current popular weather models including Fifth Generation Mesoscale Model (MM5), Weather Research and Forecasting-Advanced Research Weather Research Forecasting (WRF-ARW), Weather Research and Forecasting-Nonhydrostatic Mesoscale Model (WRF-NMM) and Advanced Regional Prediction System (ARPS). Currently, determining the “best” model over the most recent 24 hour period based on accuracy of the model output parameters involves extensive scripting and setup, creating a high possibility of error, and minimizing repeatability of the model execution.
Creating input meteorological data files for mesoscale modeling is a daunting task for the uninitiated researcher working on a case study. The researcher/user is required to know what data should be acquired and where to obtain it from and to run a complex sequence of scripts, modifying variables and file locations, C and FORTRAN code modifications and recompilations. Specifically, as part of the C gridded binary (GRIB) decoder, the user must indicate where the output decoded GRIB file should be placed. In the FORTRAN file, edits are required regarding central latitude and longitude, output file names, etc. Each of these code changes would be followed by recompilation. If the user mistypes any of the parameters, additional changes and recompilations must be made, leading to a highly error-prone and time consuming process.
Therefore, there is a need in the art for a method and apparatus for determining accuracy of mesoscale weather modelsin an easy-to-use and efficient manner.